SHF: Small: Collaborative Research:Concurrent Programming with Composable Transactional Objects

SHF：小型：协作研究：使用可组合事务对象进行并发编程

• 批准号: 1421126
• 负责人: Eric Koskinen
• 金额: $ 25万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1421126&HistoricalAwards=false
• 关键词: SHF; Small; Collaborative; Research; Concurrent

SHF: Small: Collaborative Research: Concurrent Programming with Composable Transactional ObjectsWith multicore architectures becoming increasingly prevalent, the problem of constructing scalable and efficient concurrent software has attracted increasing attention. There has been growing interest programming models that allow programmers to demarcate regions of thread code---so-called transactions---that should appear to occur atomically, when viewed from the perspective of other threads.The premise of this project is that current, monolithic software transactional memory (STM) designs are inherently too inefficient and permit too little parallelism. Instead we propose a very different approach: a library of customized concurrent data structures that can be composed, through a very light-weight run-time, to form transactions. Each data structure is optimized to exploit the semantics of its type. The intellectual merits are the development of new type-specific synchronization and recovery algorithms, along with formal tools to reason about their correctness. These ideas will be embodied in a novel concurrency library and verification toolkit, which will be used to construct benchmarks and applications. The boarder impacts involve incorporating concurrency into education and the potential to benefit society through higher performing, more reliable, and less expensive software.

SHF：小：合作研究：随着多核体系结构的日益普及，构建可扩展的高效并发软件的问题越来越受到关注。 有越来越多的兴趣编程模型，允许程序员划分区域的线程代码-所谓的事务-应该出现原子，从其他threads.The前提下，该项目是，目前，单片软件事务存储器（STM）设计固有的效率太低，允许太少的并行性。 相反，我们提出了一个非常不同的方法：一个定制的并发数据结构库，可以组成，通过一个非常轻量级的运行时，形成事务。 每个数据结构都经过优化，以利用其类型的语义。 智能的优点是新的类型特定的同步和恢复算法的发展，沿着正式的工具，以理由对他们的正确性。 这些想法将体现在一个新的并发库和验证工具包，这将被用来构建基准和应用程序。 更广泛的影响包括将并发纳入教育，以及通过更高性能，更可靠和更便宜的软件造福社会的潜力。